Uniform a priori bounds and error analysis for the Adam stochastic gradient descent optimization method

• URL: http://arxiv.org/abs/2603.18899v1
• Date: Thu, 19 Mar 2026 13:39:53 GMT
• Title: Uniform a priori bounds and error analysis for the Adam stochastic gradient descent optimization method
• Abstract summary: The adaptive moment estimation (Adam) proposed by Kingma & Ba (2014) is presumably the most popular gradient descent (SGD) optimization method for the training of deep neural networks (DNNs) in artificial intelligence (AI) systems.<n>Previous error analysis results for strongly convex optimization problems (SOPs) rely on the assumption that Adam does not diverge to infinity but remains uniformly bounded.<n>It is the key contribution of this work to establish uniform a priori bounds for Adam and, thereby, to provide -- for the first time -- an unconditional error analysis for Adam for a large class of strongly convex SOPs.
• Score: 1.8734449181723825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The adaptive moment estimation (Adam) optimizer proposed by Kingma & Ba (2014) is presumably the most popular stochastic gradient descent (SGD) optimization method for the training of deep neural networks (DNNs) in artificial intelligence (AI) systems. Despite its groundbreaking success in the training of AI systems, it still remains an open research problem to provide a complete error analysis of Adam, not only for optimizing DNNs but even when applied to strongly convex stochastic optimization problems (SOPs). Previous error analysis results for strongly convex SOPs in the literature provide conditional convergence analyses that rely on the assumption that Adam does not diverge to infinity but remains uniformly bounded. It is the key contribution of this work to establish uniform a priori bounds for Adam and, thereby, to provide -- for the first time -- an unconditional error analysis for Adam for a large class of strongly convex SOPs.

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